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991.
Reza Ghaemi Nasir bin Sulaiman Hamidah Ibrahim Norwati Mustapha 《Artificial Intelligence Review》2011,35(4):287-318
The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised
classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering
solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To
date, significant progress has been contributed to find consensus clustering that will yield better results than existing
clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction
of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided
clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies
among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in
clustering ensemble improves the clustering accuracy and addresses open questions subject to future research. 相似文献
992.
Ahmad?Reza?KhoogarEmail author Alireza?K.?Tehrani Mehdi?Tajdari 《Journal of Intelligent and Robotic Systems》2011,63(1):101-113
This paper presents a dual neural network for kinematic control of a seven degrees of freedom robot manipulator. The first
network is a static multilayer perceptron with two hidden layers which is trained to mimic the Jacobian of a seven DOF manipulator.
The second network is a recurrent neural network which is used for determining the inverse kinematics solutions of the manipulator;
The redundancy is used to minimize the joint velocities in the least squares sense. Simulation results show relatively good
comparison between the outputs of the actual Jacobian matrix and multilayer neural network. The first network maps motions
of the seven joints of the manipulator into 42 elements of the Jacobian matrix, with surprisingly smaller computations than
the actual trigonometric function evaluations. A new technique, input-pattern-switching, is presented which improves the global
training of the static network. The recurrent network was designed to work with the neural network approximation of the Jacobian
matrix instead of the actual Jacobian. The combination of these two networks has resulted in a time-efficient procedure for
kinematic control of robot manipulators which avoids most of the complexity present in the classical-trigonometric-based methods.
Also, by electronic implementation of the networks, kinematic solutions can be obtained in a very timely manner (few nanoseconds). 相似文献
993.
Jahan Bakhsh RaoofAuthor Vitae Reza OjaniAuthor VitaeSeyed Mahdi GolabiAuthor Vitae Ezat Hamidi-AslAuthor VitaeMohammad Saeid HejaziAuthor Vitae 《Sensors and actuators. B, Chemical》2011,157(1):195-201
Development of an electrochemical biosensor based on peptide nucleic acid (PNA) probe for detection of target DNA sequence and single nucleotide mutation in p53 tumor suppressor gene corresponding oligonucleotide using methylene blue (MB) as an electrochemical indicator is described. The interaction between MB and short sequence of p53, one of the most important tumor suppressor genes due to its dysfunction in the majority of human cancers, was studied by differential pulse voltammety (DPV). Probe modified electrode was prepared by self-assembled monolayer (SAM) formation of thiolated PNA molecules on the surface of gold electrode (GE). The hybridization of PNA probe with target DNA was performed in solution to form PNA-DNA hybrid on the surface of the GE. A significant increase in the reduction signal of MB was observed upon hybridization of the probe with the complementary DNA. The selectivity of the biosensor was studied using noncomplementary oligonucleotides. Furthermore, our results confirmed the ability of the sensor to detect single base mismatch in the sample oligonucleotide. The influence of probe concentration on the effective discrimination against noncomplementary sequence and point mutation was also investigated. Diagnostic performance of the biosensor is described and the detection limit is found 6.82 × 10−10 M. The electrochemical impedance spectroscopy was also employed to further investigate the sensor function. 相似文献
994.
This paper presents a novel heuristic method for solving an extended Markowitz mean–variance portfolio selection model. The extended model includes four sets of constraints: bounds on holdings, cardinality, minimum transaction lots and sector (or market/class) capitalization constraints. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio is equal to a predefined number. The sector capitalization constraints reflect the investors’ tendency to invest in sectors with higher market capitalization value to reduce their risk of investment.The extended model is classified as a quadratic mixed-integer programming model necessitating the use of efficient heuristics to find the solution. In this paper, we propose a heuristic based on Particle Swarm Optimization (PSO) method. The proposed approach is compared with the Genetic Algorithm (GA). The computational results show that the proposed PSO effectively outperforms GA especially in large-scale problems. 相似文献
995.
Data Envelopment Analysis (DEA) is a managerial powerful tool to evaluate the relative efficiency of each decision making unit (DMU). Nowadays, multi-objective DEA models in static environment are an attractive technique for evaluation quantity and quality aspects of performance analysis because there is some weakness in single objective DEA such as one-dimensional performance analysis and also it is important to consider the decision maker(s) preference over the potential adjustments of various inputs and outputs when DEA is employed. In this paper, a fuzzy dynamic multi-objective DEA model is presented in which data are changing sequentially. This paper assesses the performance of the railways using presented model as a numerical example to evaluate the results of the model. Results indicate that the multiple objective program model improves discriminating power of classical DEA models with just one time calculation of the efficiency achievement for all DUMs. 相似文献
996.
The quantitative performance evaluation of different deployments of distributed software objects over computational nodes is one of the main activities during the early stages of the design phase and should be supported by automated tools. The important design decision is to finding the optimal placement of objects, from the performance viewpoint, for different input workloads. Each deployment of objects may impose two kinds of delay on the overall performance of the software: first, the communicational delay due to the remote invocations among distributed objects and second, the computational delay due to the resource sharing by two or more concurrently executing object invocations. The object deployment problem can be formulated as an optimization problem to find the optimal deployment for which the total delay is minimal. In this paper an analytical model for delay prediction of object deployments considering the input workload of the software is presented. This model applies the object-oriented load metrics such as object population and object utilization to estimate the total amount of delay corresponding to a given object deployment. To achieve this, a novel method, called delay propagation, is proposed to compute the amount of delay corresponding to each method invocation which affects the overall response time of the software. In order to verify the proposed analytical delay predictor model, a statistical regression-based method is used. Moreover, by comparing the proposed method with the existing deployment optimization methods, which apply the Layered Queueing Networks to evaluate the performance of each deployment in the search space, a significant improvement in efficiency is observed due to the fast evaluation of each deployment instance in the search space. 相似文献
997.
The main contribution of this paper is to propose a nonlinear robust controller to synchronize general chaotic systems, such that the controller does not need the information of the chaotic system’s model. Following this purpose, in this paper, two methods are proposed to synchronize general forms of chaotic systems with application in secure communication. The first method uses radial basis function neural network (RBFNN) as a controller. All the parameters of the RBFNN are derived and optimized via particle swarm optimization (PSO) algorithm and genetic algorithm (GA). In order to increase the robustness of the controller, in the second method, an integral term is added to the RBF neural network gives an integral RBFNN (IRBFNN). The coefficients of the integral term and the parameters of IRBFNN are also derived and optimized via PSO and GA. The proposed methods are applied to the famous Lorenz chaotic system for secure communication. The performance and control effort of the proposed methods are compared with the recently proposed PID controller optimized via GA. Simulation results show the superiority of the proposed methods in comparison to the recent one in improving synchronization while using smaller control effort. 相似文献
998.
Mansour Sheikhan Reza Shahnazi Ali Nooshad Yousefi 《Neural computing & applications》2013,23(5):1359-1368
Wind energy conversion systems can work by fixed and variable speed using the power electronic converters. The variable-speed type is more desirable because of its ability to achieve maximum efficiency at all wind speeds. The main operational region for wind turbines according to wind speed is divided into partial load and full load. In the partial-load region, the main goal is to maximize the power captured from the wind. This goal can be achieved by controlling the generator torque such that the optimal tip speed ratio is tracked. Since the wind turbine systems are nonlinear in nature and due to modeling uncertainties, this goal is difficult to be achieved in practice. The proportional-integral (PI) controller, due to its robustness and simplicity, is very often used in practical applications, but finding its optimal gains is a challenging task. In this paper, to cope with nonlinearities and at the same time modeling uncertainties of wind turbines, a PI torque controller is proposed such that its optimal gains are derived via a novel scheme based on particle swarm optimization algorithm and fuzzy logic theory. The proposed method is applied to a 5-MW wind turbine model. The simulation results show the effectiveness of the proposed method in capturing maximum power in the partial-load region while coping well with nonlinearities and uncertainties. 相似文献
999.
Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) is a promising technique to congestion control for reducing packet loss and improving network utilization in transmission control protocol (TCP)/Internet protocol (IP) networks. AQM policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts, and the adoption of a suitable control policy. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF as a nonlinear controller is suitable as an AQM scheme to control congestion in TCP communication networks since it has nonlinear behavior. Particle swarm optimization (PSO) algorithm is also employed to derive RBF output weights such that the integrated-absolute error is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The output weights and the coefficient of the integral error term in the latter controller are also optimized by PSO algorithm. It should be noted that in both proposed controllers the parameters of radial basis functions are selected to symmetrically partition the input space. The results of the comparison with adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small. 相似文献
1000.